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To identify what influences the attitudes and behavior of customers, most companies rely on surveys, focus groups, and ethnographic research. The trouble is, surveys and focus groups tap customers’ memories, which are unreliable, and the presence of observers can cause customers to alter their behavior. The authors, three academics, believe they have found a new research tool without those flaws: real-time experience tracking. Conducted over mobile phones, RET allows companies to inexpensively collect instant, unbiased feedback from customers 24 hours a day.

In RET, participants supply the answers to a four-question survey every time they encounter a brand, be it through a direct interaction, such as a purchase or ad, or an indirect one, such as a conversation with another customer. The process is incredibly simple: They need only text a four-character message. One major benefit is that RET allows firms to track campaigns as they unfold and readjust them toward the most effective tactics.

You really need to know only four things about each customer encounter with your brand.

This article describes how a growing number of companies, such as Schweppes, Energizer, and Fox, are using RET to inform their marketing decisions, increase sales, and help customers improve their own experiences.

Few marketing challenges are tougher than identifying and influencing what drives customers’ attitudes and behavior. Traditionally, executives have relied on a combination of quantitative data from surveys (such as those that track customer satisfaction and brand image) and qualitative insights from focus groups and interviews.

Unfortunately, both kinds of research suffer from a fundamental flaw: They rely on customers’ memories, which decay rapidly. Consumers frequently recall a company’s communications inaccurately; it’s not uncommon for people to claim they’ve seen a company’s TV ad at a time when the firm was not advertising. And even genuine memories are often biased by context: If a customer has made a major purchase, she’s more likely to remember her experience of the transaction positively in order to feel good about the purchase. Internet-based research tools suffer less from these problems because they can capture customer experiences almost immediately, before memory fades or becomes biased, but they can be used only with online interactions, which account for just 15% of customers’ encounters with companies and their brands.

The only traditional technique that really allows companies to record the complete range of customer experiences is ethnographic research, in which researchers shadow individual consumers and watch their behavior. This approach, however, is both labor-intensive and expensive, and it’s also potentially misleading: It’s hard to untangle the individual customer’s quirks from general customer behavior. Worse, the ethnographic approach introduces another bias: The customer will probably have an unconscious desire to please the researcher, who is physically present, which will affect her reactions. Corporations, therefore, face a dilemma. They must either rely on imperfect and biased memories or risk spending a ton of money on directly observing potentially unrepresentative behavior. Either way, the insights and data on which they base their marketing decisions are inherently faulty.

Marketers have long sought a research method that can capture customer reactions immediately, does not intrude into those reactions, minimizes bias, and can affordably be applied to customers in relatively large numbers. We believe that real-time experience tracking (RET), a new research tool, rises to this challenge.

Over the past two years a number of leading companies—including Unilever, BSkyB, PepsiCo, Schweppes, HP, Energizer, Microsoft, InterContinental Hotels, and SAS—have been using RET to inform their marketing decisions. For example, when Schweppes bought Abbey Well, a small independent UK brand of mineral water, it launched an ambitious growth campaign that began with a series of topical advertisements. Thanks to RET, executives realized within a week that the most successful ads focused on a “Schwim Free” offer giving anyone with a Schweppes Abbey Well bottle cap free entry on Mondays to a public indoor swimming pool (a valuable proposition in a climate where it’s often too cool to swim outdoors). So Schweppes immediately poured more resources into that part of the campaign, extending it to more pools and all weekdays. Ultimately, 175,000 people took advantage of the offer, and within a year sales of Schweppes Abbey Well had grown by 35%. Furthermore, the promotion received a lot of press coverage and generated health associations for the Schweppes brand, helping the sales of other Schweppes products.

This is just one of the projects that we and the market research agency MESH Planning, which developed the RET data collection method, have conducted. Over the past two years, we have studied the impact of those projects and advised MESH and its clients on how best to gather and draw insights from RET data. In total we have gathered information on more than 750,000 company-customer interactions in sectors as diverse as entertainment, telecommunications, financial services, electrical appliances, automobiles, personal care, food and beverages, and charities. Though MESH is the only agency we know of that is conducting RET in the applications we’ll describe here, other organizations are adopting some of the principles behind it in their research, and we expect this approach to spread rapidly.

Let’s turn now to how the tool works.

Designing the Program

Real-time experience tracking was born of two insights. First, while a market researcher can’t easily follow customers around 24 hours a day, those customers’ cell phones can, and unlike human observers, they don’t sway people’s perceptions of experiences. The second insight was that although customers may interact with a company in thousands of ways, you really need to know only four things about each encounter: the brand involved, the type of touchpoint (TV ad, say, or call to the service center), how the participant felt about the experience, and how persuasive it was. (Did it make the customer more inclined to choose the brand next time?)

We developed, therefore, a quick SMS-based microsurvey that customers can take on their mobile phones every time they encounter a company’s brand—whether in making a transaction, seeing an advertisement, or even in an informal conversation about the brand with other people. The survey requires participants just to input a four-character text message. And in an age when more and more people are texting and tweeting about their personal experiences all the time, a four-character text can hardly be described as intrusive.

In the programs we studied, a few hundred consumers were recruited to participate. The participants completed four phases of research:

1. They filled out an online questionnaire about their awareness, knowledge, perception, and use of the company’s brand or product and those of four or five competitors (without knowing which firm was commissioning the research).

2. They texted a four-character message whenever they came across any of the brands over the course of the research project (between a week and a month, depending on the likely frequency of encounters with the brands in question).

3. They were asked but not obliged to keep an online diary in which they expanded on their encounters with the brands and how they felt about them.

4. At the close of the project they completed a modified version of the first questionnaire to see whether their attitudes toward the brands in question had shifted.

The exhibit “Real-Time Experience Tracking in Action” illustrates the four phases of the program.

Real-Time Experience Tracking in Action

Home electronics is one sector where companies need to understand how diverse touchpoints add up to a customer’s decision to buy. In this typical study (a composite of several projects), a manufacturer asks 500 people to report on their encounters with five home electronics brands over the course of a month. Here’s what each participant does:

1. Fills Out a Survey

Answers questions about her awareness, knowledge, perceptions, and use of the brands.

2. Texts Feedback

Reports on her encounters with the brands by cell phone. For each encounter she inputs just four characters: a letter indicating the brand involved, a second letter for the type of encounter, a numerical score indicating how positive she feels about the encounter, and another score for how persuasive it was.

3. Describes Encounters

Elaborates on her encounters in an online diary, which displays her text responses and allows her to upload photographs of encounters when she has time in the evening. A pull-down menu lets her specify each touchpoint’s subtype—for instance, whether a word-of-mouth encounter was initiated by her or another person, or simply overheard.

4. Revisits the Survey

Completes a second questionnaire meant to uncover how her attitudes have changed as a result of her encounters with the brands.

Challenges and Limitations

To ensure a balanced, representative sample, you need to profile potential respondents through a series of questions. A sample that matches the firm’s target market on demographics and other relevant criteria can then be assembled. For example, the sample can be checked to make sure it isn’t overly skewed toward people who are technologically savvy or innovative. Other factors to consider are the respondents’ marketing literacy, assertiveness, shopping enjoyment, and confidence in obtaining information from peers online or off-line. If a sample is still skewed following those checks, the results can be adjusted by giving greater weight to responses from underrepresented sections of the target market, just as in political polling. In highly diverse sectors, a larger sample is sometimes needed so that different segments (say, middle-aged Mexican women versus young Italian men) can be analyzed separately.

In setting up the text surveys, it’s important to provide a reasonably comprehensive list of touchpoint types, covering both direct encounters, such as sales visits, conversations with call centers, visits to the firm’s website, purchases, and so on, and indirect ones, such as contact with other customers, seeing the brand in the news, or interactions with the firm’s agents, distributors, or retailers. Mediated interactions often have a huge impact. The most positively received TV ad we’ve seen in our research, for example, was for an alcoholic beverage—but the ads were not the brand’s. They were placed by a major supermarket, in a Christmas promotion. The respondents’ texts and diaries showed that the ad, which offered the product at a substantial discount, did not devalue the brand: Consumers assumed that the retailer was simply running the promotion as a loss leader. It’s also very helpful to have an “other” category for touchpoints that cannot be predicted, which participants can elaborate on in their diaries.

Participation in the surveys inevitably raises respondents’ awareness of the product category during the study period. So the purpose of the second questionnaire is to unearth the relative changes in respondents’ awareness, knowledge, perception, and use of the various brands or products. You can also deal with the problem by taking a randomized control group from the initial sample. The participants in this group skip the text messages, simply filling in an adapted survey at the start and end of the study period. Shifts in their attitudes or key behaviors over that time frame can then be compared with those of the main group.

One might expect that it would be hard for RET participants to remain engaged in the program, given the level of commitment needed. But most find that the minimal extra effort involved is outweighed by the engaging nature of the process, and many report that they enjoy reflecting on their customer journey. We believe this can be explained by the fact that they initiate the microsurveys themselves. Respondents to traditional surveys, by contrast, find many of the questions and touchpoints completely irrelevant.

Even relatively passive touchpoints made differences in customer behavior. People who saw the product in a friend’s house, for instance, were three times as likely to buy it as people who didn’t.

Clearly there are limitations to our approach, and we expect it to evolve. For instance, when doing research in some sectors, we have to vary what we ask participants to score encounters on. With nonprofits, we ask participants to provide a score for how much they learned about a charity from the encounter, rather than how positive they felt about the encounter. Also, we cannot assess the effect of an individual consumer’s media consumption habits without burdening the consumer with more questions, or track the location of the touchpoints purely through a text message. It is, of course, possible to track some such details through smartphones, but using only participants who owned them would at this time make obtaining a balanced sample difficult. More fundamental is the problem of touchpoints whose contexts preclude texting. For example, tracking real-time contact with an airline is a challenge because the use of mobile phones on planes in flight remains very limited. We are confident, however, that technological innovations will enable us to find ways around such problems.

What the Data Tell You

A benefit of tracking an individual over time is that you can often cover a complete customer journey from the identification of a need to a purchase. And with statistical tools, you can use RET data to identify not only what most motivates customers to buy your brand but also how various touchpoints combine in a chain to influence the customers’ decisions. Here are some useful analyses you can conduct:

Key drivers.

Applying simple regression analysis to the RET data can quickly tell you which touchpoints are most closely correlated with individual customer behaviors, such as a request for more information or an actual purchase. A good way to present this information is in a diagram called an odds analysis, which quantifies—and compares—the relative likelihood that various touchpoints will lead to the behavior in question.

The exhibit “Analyzing the Odds” compares the effects of several touchpoints on the decision to buy a particular home electronics brand. Predictably, people who noticed the brand when browsing in a store were far more likely to buy it, whether online or in another store visit, than people who didn’t. But even relatively passive touchpoints, such as hearing about the brand from other customers, direct mailings, and TV ads, made appreciable differences to customer behavior. People who saw the product in a friend’s house during the study period, for instance, were three times as likely to buy it as people who didn’t.

Analyzing the Odds

An odds analysis uses data gathered through real-time experience tracking to determine which touchpoints influence customer behavior most. Here’s what a typical analysis (based on a composite of several studies) for a home electronics product might look like. It reveals that in-store visits have the most impact: People who have come across the product in a store are 7.5 times as likely to buy this brand as people who have not.

Competitive analysis.

You can also see how effective your touchpoints are at driving behavior and shaping attitudes relative to the touchpoints of your competitors. A good way to present this is with a touchpoint impact matrix, shown in the exhibit “Checking Out the Competition,” which compares the performance of five touchpoints of two UK cell-phone-network providers. From this comparison, one can see that although people like Brand A’s TV and newspaper advertisements, these have only a limited impact on purchases, because many consumers are also hearing negative comments from current customers who are unhappy with coverage, packages, and responsiveness. Brand B has less word of mouth, but what word of mouth does occur is very positive. It is clear that Brand A’s communications spending is going to be largely wasted until the company fixes its service problems, so it would do better to divert investment from marketing into service and operations.

Checking Out the Competition

This comparison of two similar cell-phone-network brands reveals that their most effective touchpoints are markedly different. It also shows that when it comes to moving customers closer to purchase, Brand A clearly is underperforming Brand B, which has invested consistently in service.

Note that you can apply versions of this technique to outcomes other than purchase, such as overall brand perception or willingness to recommend the brand.

Chains of touchpoints.

Customers’ reactions to each exchange are shaped by their previous interactions with a brand. The decision to go into a store might result from a conversation with a friend, seeing the store window display, or something else. Of course, the act of visiting a store increases the chances of making a purchase. And the quality of the purchasing experience will in turn influence the likelihood that the customer will buy the brand or product again or recommend it.

RET data can indicate where such chains might be broken. For instance, when the moviemaker Fox looked at its RET feedback, it saw that its spending on posters and newspapers was relatively ineffective, though some designs worked better than others. The data also showed that movie trailers were effective at getting cinemagoers into theaters, but viewing them online rather than on TV or in the cinema had the great advantage of being only a few clicks from ticket purchase—and hence drove more sales. As a result, Fox allocated more spending online and started to steer customers toward trailers on YouTube or Facebook in its posters and ads. Those moves led to a big increase in digital views of trailers, which strongly predicted purchases.

From Real-Time Insight to Real-Time Action

Insights gained from RET can be acted on immediately—a great advantage in new product launches or marketing campaigns conducted in fast-changing environments.

Energizer’s introduction of a new Schick Hydro razor in Germany was intended to bring about a step change in brand performance. The launch team used RET during the 12-week campaign, adapting its strategies along the way on the basis of feedback gathered. An analysis of the first few weeks of data revealed several opportunities for improvement: For example, shifting the focus from TV advertising to online spending would increase purchases among the young male target market. The data also suggested that print-media advertising was less cost-effective than TV sponsorship (through extensive product placement in a TV show). The launch team retooled the second half of the campaign by increasing TV sponsorship, redesigning TV ads, and adding supporting online activities, such as pop-up ads with the same theme as the TV ads. As a result, the campaign achieved greater impact with lower spending. Energizer executives calculated that the new measures led to a threefold improvement in advertising cost-effectiveness and increased Energizer’s revenue in the razor category by 10% in less than four months. And the lessons learned from the launch held considerable value for the company’s other brand campaigns, especially given that Energizer had significantly less to spend on communications than its main competitor, the market leader.

Responding to RET findings in real time is easier in theory than in practice, however. That’s because RET covers the complete customer journey, which means the data it generates are useful to virtually every customer-facing part of a firm—from marketing communications and PR to operations and service delivery. Reaching, mobilizing, and coordinating all the relevant decision makers, therefore, presents a huge challenge, especially for products and services that are promoted and delivered through multiple channels over large areas.A tremendous amount of valuable work has been done on improving our ability to track and learn from customer behavior with the help of new technology. But as market researchers, we’ve found that the emphasis on behavior has rather dominated the literature and practice. Real-time experience tracking, which goes beyond recording behavior to help us gain insight into the rich perceptual and emotional worlds human beings live in, helps redress the imbalance. And because RET enables companies to assess and respond in real time to customers’ reactions to products, services, or branding efforts, it can play a central role in allowing customers to help design their own experiences with products. As RET and tools like it emerge, we expect that marketing will cease to be a game of stimulus-response and will evolve into a continual process of cocreation.

Emma K. Macdonald is a senior lecturer at Cranfield University’s School of Management in the UK, the research director of the Cranfield Customer Management Forum, and an adjunct fellow of the Ehrenberg-Bass Institute at the University of South Australia.

Hugh N. Wilson is a professor at Cranfield School of Management and the director of the Cranfield Customer Management Forum.

Umut Konuş is an assistant professor at the Eindhoven University of Technology in the Netherlands.

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